ExploreGS: a vision-based low overhead framework for 3D scene reconstruction
원문 초록
This paper proposes a low-overhead, vision-based 3D scene reconstruction framework for drones, named ExploreGS. By using RGB images, ExploreGS replaces traditional lidar-based point cloud acquisition process with a vision model, achieving a high-quality reconstruction at a lower cost. The framework integrates scene exploration and model reconstruction, and leverags a Bag-of-Words(BoW) model to enable real-time processing capabilities, therefore, the 3D Gaussian Splatting (3DGS) training can be executed on-board. Comprehensive experiments in both simulation and real-world environments demonstrate the efficiency and applicability of the ExploreGS framework on resource-constrained devices, while maintaining reconstruction quality comparable to state-of-the-art methods.
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R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
HRGS: Hierarchical Gaussian Splatting for Memory-Efficient High-Resolution 3D Reconstruction
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting
SPC to 3D: Novel View Synthesis from Binary SPC via I2I translation
토론
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